SandboxAQ Reaches Significant Milestone in Advancing Battery Shelf-Life Testing and Predictive Maintenance for the U.S. Army

Large Quantitative Models powered by 2 million hours of battery testing data will enable rapid and accurate predictions of battery performance, improve inventory and quality control processes, and ensure optimal mission performance and readiness

PALO ALTO, Calif. , Dec. 9, 2024 /PRNewswire/ -- SandboxAQ announced today it has reached a significant milestone in predicting advanced battery shelf-life testing and predictive maintenance for the U.S. Army Combat Capabilities Development Command (DEVCOM). Working closely with the Army's Command, Control, Communications, Computers, Cyber, Intelligence, Surveillance and Reconnaissance ( C5ISR ) Center, SandboxAQ has compiled a comprehensive dataset of battery performance and predictive AI processes that can be used to assess the status of the lithium-ion batteries used in a wide range of Army applications.

In support of the Army's Power and Energy Modernization initiatives, the dataset created by SandboxAQ and C5ISR Center consists of more than 2 million hours of lab and simulated testing of 18650 cylinder cells, using AI to simulate various real-world use conditions and scenarios such as battery aging at different temperatures and durations, discharge rate, and more. This data, in conjunction with other proprietary tools, will train SandboxAQ's Large Quantitative Models (LQMs) to ensure that lithium ion batteries used in electric vehicles, robotic platforms, communications, and other portable devices meet rigorous Army shelf-life requirements, ranging from 2 to 20 years.

The traditional method of estimating battery shelf-life involves conducting proxy tests on batteries at various states of charge and using extrapolation to estimate shelf-life across the Army's vast inventory of batteries. Such proxy methods may not always accurately predict real-world battery performance – especially after long periods of storage or non-use. Inaccurate shelf-life predictions could result in the premature disposal of good batteries, which increases material costs and complexity, or the fielding of inferior batteries, which could compromise mission objectives or jeopardize soldiers and equipment.

SandboxAQ's LQMs will use collected battery test data to significantly reduce the time required for shelf-life testing while enabling precise predictions of battery performance and maintenance requirements. In storage, the Army could use LQMs to test new batteries to ensure they meet quality and shelf-life requirements. In the field, future battery chargers could send data to the AI model to provide information on battery performance, remaining life-span, maintenance needs, and other details directly to the warfighter or unit so they can determine if battery replacement is required.

"Most commercial battery applications do not have the same rigorous performance or shelf-life requirements as those intended for military use, so most cell manufacturers do not take shelf-life into consideration when designing advanced battery chemistries or sourcing materials," said Ang Xiao, Technical Lead, AI & Quantum Application at SandboxAQ. "The comprehensive battery dataset we've compiled with C5ISR Center will add this new predictive capability to our Large Quantitative Models, enabling all of our customers and partners to benefit from these previously unavailable insights."

In October, SandboxAQ announced that its LQMs reduced the time needed to predict lithium-ion battery end-of-life (EOL) by 95%, with 35x greater accuracy and 50x less data than traditional approaches – reducing the time for cell lifetime testing from months or years to just days. These results indicate LQM-informed predictive lifetime models could potentially shave years off of a new cell's development and commercialization timeline and save cell manufacturers millions of dollars in R&D costs. These savings would translate into faster innovation cycles, enabling the advancement of battery technology across multiple industries and accelerating the adoption of new solutions to meet the growing demand for high-performance energy storage.

For more information about the impact of Large Quantitative Models in the battery industry, please visit https://www.sandboxaq.com/solutions/large-quantitative-models, or visit us at the Advanced Automotive Battery Conference (Booth #123; Dec. 9-12 in Las Vegas).

About SandboxAQ
SandboxAQ is a B2B company delivering AI solutions that address some of the world's greatest challenges. The company's Large Quantitative Models (LQMs) deliver critical advances in life sciences, financial services, navigation, cyber and other sectors. The company emerged from Alphabet Inc. as an independent, growth capital-backed company in 2022, funded by leading investors including T. Rowe Price, Eric Schmidt, Breyer Capital, Guggenheim Partners, Marc Benioff, Thomas Tull, Section32, and others. For more information, visit http://www.sandboxaq.com.

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SOURCE SandboxAQ